This proposal describes a new method for the automatic processing of polygraphic sleep records by computer. It includes a number of pattern recognition algorithms for the processing of the electroencephalographic (EEG) data as well as the associated electronics for the processing of electromyographic (EMG and electroculargraphic (EOG) data. The EEG pattern recognition algorithms presently recognize alpha, beta, theta, and delta rhythms and sleep spindles. These algorithms have been incorporated in a PDP-12 computer program which processes the EEG on-line in a real-time environment, and obtains the sleep stage for successive 20-second epochs using the criteria of Rechtschaffen and Kales. Comparision of the computer results for three subjects with the visually obtained sleep scores for non-REM sleep indicates that the proposed method has an overall sleep staging accuracy of 94%, an accuracy which is significantly higher than that of alternative methods which have been described in the literature. Possible improvements to the existing algorithms are outlined. The preliminary results lead us to expect that with the planned incorporation of the EMG and EOG electronics for detection of REM sleep the proposed system will be an inexpensive automated method for the precise classification of sleep stages. It is proposal that the system be completed and that an extensive evaluation of its capabilities by conducted.